2. Two General Types of
Sampling:
Probability sampling - is taking a sample
from the population.
• It ensures that there is a possibility for
each person in a sample population to be
selected
3. Types of Probability Sampling
• Random Sampling – This is similar to
lottery method that provides everyone in
the population the equal chance to be
picked as sample.
• Systematic Sampling – This is used if a
high density of a population is at stake.
4. • Stratified Random Sampling - dividing up the
population into smaller groups, and randomly
sampling from each group.
• Cluster Sampling - is similar to stratified
sampling because the population to be sampled
is subdivided into mutually exclusive groups.
However, in cluster sampling the groups are
defined so as to maintain the heterogeneity of
the population.
Example: Female members of Baranggay San
Isidro
5. Non-Probability Sampling
• Non-probability sampling represents
a group of sampling techniques that help
researchers to select units from
a population that they are interested in
studying. Collectively, these units form
the sample that the researcher studies
6. Types of Non-Probability Sampling
Network sampling – “referral sampling”
that stems from one or few identified
samples who after being involved in the
study will lead the researcher to other
samples who possess the same
attributes.
“word of mouth" approach of acquiring
participants.
7. • Accidental Sampling - A sampling by
opportunity in which the researcher takes the
respondents from those he meets
unexpectedly.
• Purposive Sampling – “Judgmental
sampling”. A deliberate selection of
individuals by the researcher based on
predefined criteria
8. • Convenience Sampling – Selecting respondents
in the easiest way. The respondents may be
the nearest people, friends, relatives,
accessible organization, available person.
• Quota Sampling - A sampling method of
gathering representative data from a group.
9. Determining the Sampling Size
Slovin formula
n = N
1+N(e)2
Where:
n=no.of sample
N= no. population
e = margin of error
**The margin of error may be .01 to .05. But the lower the
margin of error, the higher the accuracy of the result.
10. Activity:
Let’s say, you want to get a sample population of all
HRM students.
1st yr. – 440
2nd yr. – 400
3rd yr. – 330
4th yr – 275
Irregular – 100
Margin or error is 3%
Editor's Notes
Sampling refers to taking a representative subsection of the population. Contacting, questioning, and obtaining information from a large population, such as the 370,000 households residing in Antipolo City, is extremely expensive, difficult, and time consuming. A properly designed probability sample, however, provides a reliable means of inferring information about a population without examining every member or element
Example: If you wanted the opinions of an HRM students a probability sample would mean that every HRM students would have an equal chance of participating in the research.
Non-probability sampling comes in various shapes and sizes, but the essence of it is that a bias exists in the group of people you are surveying. Let’s think about it in the context of our fictional color preference survey. If I asked the question to all of my friends, the results are not representative of anything other than the opinion of my friends and, specifically, those friends to whom I decided to send the survey. Another example of non-probability sampling would occur if I were to send you the survey and then ask you to pass the survey onto a friend. This effect, called snowballing, creates a biased sample wherein not everyone has an equal chance of being sampled.
QUOTA - For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60. This means that individuals can put a demand on who they want to sample (targeting).
Determining sample size is a very important issue because samples that are too large may waste time, resources and money, while samples that are too small may lead to inaccurate results. There is no general rule regarding the sample size. However, we can say that the higher the percentage, the higher the validity. It is natural to say that the bigger the population, the lesser percentage of the sample is taken.
N = 1545 n= 1545 E = (.03)2 = .0009 ---------- 1+1545 (.0009) 1545 1545 ------------------ n = ---------------- 1+ 1.3905 2.3905 n= 646
